Sign up to receive free email alerts when patent applications with chosen keywords are publishedSIGN UP

Abstract:

Operation of a patient's heart or lungs may be analyzed by transmitting
ultrasound energy into the patient's lung, and detecting Doppler shifts
of reflected ultrasound induced by moving borders between blood vessels
in the lung and air filled alveoli that surround the blood vessels.
Movement of the border is caused by pressure waves in the blood vessels
that result in changes in diameter of those blood vessels. The detected
Doppler shifts are used to obtain power and velocity data at each of a
plurality of different air pressure levels, and the pulmonary blood
pressure of the patient is then estimated based on the obtained data.

Claims:

1. A method of estimating a pulmonary blood pressure of a patient,
comprising the steps of: sequentially obtaining, using transthoracic
pulmonary Doppler ultrasound, power and velocity data from at least one
of the patient's lungs at each of a plurality of different air pressure
levels; and estimating the pulmonary blood pressure of the patient based
on the data obtained in the obtaining step.

2. The method of claim 1, wherein the obtaining step comprises obtaining
power and velocity data from at least two different locations in the at
least one of the patient's lungs.

5. The method of claim 1, wherein the estimating step comprises
identifying at least one of the air pressure levels at which a total
power approaches zero.

6. The method of claim 1, wherein the estimating step comprises
identifying at least one of the air pressure levels at which a total
power is less than 10% of a total power obtained when the air pressure
level in the at least one of the patient's lungs is not elevated.

7. The method of claim 1, wherein the obtaining step comprises obtaining
power and velocity data from at least two different locations in the at
least one of the patient's lungs, and the estimating step comprises
applying a classification algorithm to the data obtained in the obtaining
step.

8. The method of claim 1, wherein the obtaining step comprises obtaining
power and velocity data from ICS2, ICS 4, and ICS 6 in the at least one
of the patient's lungs, and the estimating step comprises applying a
classification algorithm to (a) a ratio of peak velocities in feature #3,
between ICS 4 and ICS 6, (b) a ratio of power integral values in feature
#3, between ICS 4 and ICS 2, (c) a ratio of peak velocities in feature
#1, between ICS 2 and ICS 6, and (d) a ratio of power integral values in
feature #5, between ICS 4 and ICS 6, wherein feature #1 appears shortly
after an R wave of an ECG and coincides with the systolic ventricular
contraction, feature #3 corresponds to a diastolic rapid filling phase,
and feature #5 coincides with atrial contraction.

9. An apparatus for measuring a pulmonary blood pressure of a patient,
the apparatus comprising: a pressure sensor configured to measure the air
pressure in at least one of the patient's lungs; a transducer configured
to transmit ultrasound energy into a target region in at least one of the
patient's lungs, detect ultrasound energy reflected from the target
region, and generate an output based on the detected ultrasound energy;
and a Doppler signal processor configured to process the output of the
transducer and sequentially obtain power and velocity data from at least
one of the patient's lungs at each of a plurality of different air
pressure levels and estimate the pulmonary blood pressure of the patient
based on the obtained power and velocity data.

10. The apparatus of claim 9, wherein the power and velocity data are
obtained from at least two different locations in the at least one of the
patient's lungs.

11. The apparatus of claim 9, wherein the transducer transmits a pulsed
wave ultrasound beam with an effective cross section of at least 1/2
cm.sup.2.

12. The apparatus of claim 11, wherein the transducer transmits a pulsed
wave ultrasound beam with a pulse repetition frequency between 1 and 3
kHz.

13. The apparatus of claim 9, wherein the processor estimates the
pulmonary blood pressure by identifying at least one of the air pressure
levels at which a total power approaches zero.

14. The apparatus of claim 9, wherein the processor estimates the
pulmonary blood pressure by identifying at least one of the air pressure
levels at which a total power is less than 10% of a total power obtained
when the air pressure level in the at least one of the patient's lungs is
not elevated.

15. The apparatus of claim 9, wherein the power and velocity data are
obtained from at least two different locations in the at least one of the
patient's lungs, and the processor estimates the pulmonary blood pressure
by applying a classification algorithm to the data obtained in the
obtaining step.

16. The apparatus of claim 9, wherein the power and velocity data are
obtained from ICS2, ICS 4, and ICS 6 in the at least one of the patient's
lungs, and the processor estimates the pulmonary blood pressure by
applying a classification algorithm to (a) a ratio of peak velocities in
feature #3, between ICS 4 and ICS 6, (b) a ratio of power integral values
in feature #3, between ICS 4 and ICS 2, (c) a ratio of peak velocities in
feature #1, between ICS 2 and ICS 6, and (d) a ratio of power integral
values in feature #5, between ICS 4 and ICS 6, wherein feature #1 appears
shortly after an R wave of an ECG and coincides with the systolic
ventricular contraction, feature #3 corresponds to a diastolic rapid
filling phase, and feature #5 coincides with atrial contraction.

17. A method of determining a level of pulmonary blood pressure of a
patient, the method comprising the steps of: transmitting ultrasound
energy into at least one of the patient's lungs; detecting Doppler shifts
of reflected ultrasound energy induced by moving borders between blood
vessels in the at least one lung and air filled alveoli that surround the
blood vessels; varying the pressure of the air in the at least one lung;
monitoring how the detected Doppler shifts change in response to the
variation of pressure; and determining a level of pulmonary blood
pressure of the patient based on the changes monitored in the monitoring
step.

20. The method of claim 17, wherein the determining step comprises
determining whether or not the patient has pulmonary hypertension.

21. The method of claim 17, wherein the determining step comprises
determining at least one numeric value for the pulmonary blood pressure
of the patient.

22. The method of claim 17, wherein the transmitting step comprises:
transmitting ultrasound energy into a first location in at least one of
the patient's lungs for a period of time that corresponds to at least one
cardiac cycle; and subsequently transmitting ultrasound energy into a
second location in at least one of the patient's lungs for a period of
time that corresponds to at least one cardiac cycle.

23. A method of determining whether a patient has pulmonary hypertension,
comprising the steps of: elevating the air pressure in at least one of
the patient's lungs to a level where blood flow would be expected to drop
in a patient who does not have pulmonary hypertension; obtaining at least
one set of power and velocity data from at least one of the patient's
lungs while the air pressure is elevated; and determining, based on the
power and velocity data obtained in the obtaining step, whether a total
power is above a threshold.

24. The method of claim 23, further comprising classifying the patient as
having pulmonary hypertension when it is determined, in the determining
step, that the total power is above the threshold.

25. The method of claim 23, wherein the threshold is 20% of the total
power that is expected in situations where the air pressure in a normal
patient's lungs is not elevated.

26. The method of claim 25, further comprising elevating the air pressure
in at least one of the patient's lungs by at least 10 mm Hg.

27. The method of claim 25, further comprising elevating the air pressure
in at least one of the patient's lungs by at least 15 mm Hg.

28. The method of claim 25, further comprising elevating the air pressure
in at least one of the patient's lungs by at least 20 mm Hg.

Description:

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This Application claims the benefit of U.S. Provisional Application
61/405,454, filed Oct. 21, 2010, which is incorporated herein by
reference.

BACKGROUND

[0002] The use of ultrasound Doppler for Spectral measurement of blood
flow velocity in arteries and veins is well established. One widely used
procedures for making such measurements is based on three typical stages:
an initial identification of the target area (where flow is to be
measured) using ultrasound imaging; placement of a marker on the
appropriate position on the image; and switching the echo device from
Imaging mode to Spectral Doppler Examination mode in order to display the
flow velocities in real-time. This procedure can be used, for example, to
measure the blood flow in a pulmonary vein.

[0003] Another procedure, which is relatively new, is used for Trans
Cranial Doppler (TCD) measurements, as well as some peripheral vascular
studies. In this procedure the ultrasound beam is directly aimed at the
known location of the target, without relying on imaging. As the
structure and positioning of the human skull and its constituents are
relatively fixed and known, specific vessels such as the arteries of the
circle of Willis, at the base of the brain, are being studied in this
procedure by echo Doppler alone (i.e. without imaging). The fact that the
flow velocity measurements can be made without imaging enables one to do
the measurements through the bones of the skull that attenuate and
scatter the ultrasound beam to such an extent that practical images
cannot be obtained.

[0004] While trans-cranial Doppler measurements are now in routine use to
study structures in the brain, applying this technology
trans-thoracically monitor pulmonary vessels was heretofore considered
impossible. This is due to the fact that the lungs contain numerous air
pockets that attenuate and scatter ultrasound far more than bone. In view
of this, except for the initial, large, segments of the pulmonary vessels
that are not masked by lung tissue, arterial and venous flow velocity in
the pulmonary vasculature and the lung tissue itself have not been
studied by Doppler ultrasound.

SUMMARY

[0005] One aspect of the invention relates to a method of estimating a
pulmonary blood pressure of a patient. This method includes the steps of
sequentially obtaining, using transthoracic pulmonary Doppler ultrasound,
power and velocity data from at least one of the patient's lungs at each
of a plurality of different air pressure levels. The pulmonary blood
pressure of the patient is then estimated based on the obtained data.
Optionally, the power and velocity data may be obtained from at least two
different locations in the patient's lungs. Optionally, the estimating
step includes identifying at least one of the air pressure levels at
which a total power approaches zero or drops to less than 10% of a total
power obtained when the air pressure level is not elevated.

[0006] Another aspect of the invention relates to an apparatus for
measuring a pulmonary blood pressure of a patient. This apparatus
includes a pressure sensor configured to measure the air pressure in at
least one of the patient's lungs, and a transducer configured to transmit
ultrasound energy into a target region in at least one of the patient's
lungs, detect ultrasound energy reflected from the target region, and
generate an output based on the detected ultrasound energy. It also
includes a Doppler signal processor configured to process the output of
the transducer and sequentially obtain power and velocity data from at
least one of the patient's lungs at each of a plurality of different air
pressure levels and estimate the pulmonary blood pressure of the patient
based on the obtained power and velocity data. The options described
above may be implemented in this embodiment as well.

[0007] Another aspect of the invention relates to a method of determining
a level of pulmonary blood pressure of a patient. This method includes
the steps of transmitting ultrasound energy into at least one of the
patient's lungs, detecting Doppler shifts of reflected ultrasound energy
induced by moving borders between blood vessels in the at least one lung
and air filled alveoli that surround the blood vessels, varying the
pressure of the air in the lungs, monitoring how the detected Doppler
shifts change in response to the variation of pressure, and determining a
level of pulmonary blood pressure of the patient based on the changes
monitored in the monitoring step.

[0008] Another aspect of the invention relates to a method of determining
whether a patient has pulmonary hypertension. This method includes the
steps of elevating the air pressure in at least one of the patient's
lungs to a level where blood flow would be expected to drop in a patient
who does not have pulmonary hypertension, obtaining at least one set of
power and velocity data from the patient's lungs while the air pressure
is elevated, and determining, based on the power and velocity data
obtained in the obtaining step, whether a total power is above a
threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] FIG. 1 is a block diagram of an embodiment of a Transthoracic
Pulmonary Doppler ("TPD") System.

[0010] FIG. 2 depicts an example of an output generated by the system of
FIG. 1.

[0011] FIG. 3 is a schematically illustration of five features in the
output shown in FIG. 2.

[0021] FIGS. 10A-C depict experimental data on the average peak positive
and negative velocities for three features of a TPD output.

[0022] FIGS. 11A is a graphical representation of the velocity differences
between normal and abnormal subjects.

[0023] FIG. 11B is a graphical representation of power differences between
normal, COPD, and fibrosis subjects.

[0024] FIG. 12 is a block diagram of system for performing pulmonary blood
pressure measurements.

[0025] FIG. 13 depicts how the TPD signals change in response to
increasing lung air pressure.

[0026] FIG. 14 depicts how the TPD signals differ at different lung air
pressures.

[0027]FIG. 15 depicts how the TPD signals change in response to changes
in lung air pressure.

[0028]FIG. 16 depicts how the TPD power levels change in response to
changes in lung air pressure.

[0029] FIG. 17A illustrates that the power level reaches zero at two
different pressures.

[0030] FIG. 17B depicts a power reading for a normal subject.

[0031]FIG. 17c depicts a power reading for a subject with pulmonary
hypertension.

[0032]FIG. 18 depicts the boundaries between features determined by an
automatic feature recognition algorithm.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0033] The inventors have recognized that the pulmonary circulation and
the pulmonary light scattering properties may be significantly modified
in a large variety of cardio-pulmonary patho-physiological conditions and
diseases, and that such information may be of significant diagnostic and
therapeutic importance. The embodiments described herein are designed to
monitor the functionality of the arteries and veins in the lungs, as well
as the integrity and functionality of the lung tissues that surround
them, using Doppler ultrasound. It is referred to herein as
"Transthoracic Pulmonary Doppler" or "TPD".

[0034] FIG. 1 is a block diagram of one such embodiment. A Doppler
ultrasound machine 12 in conjunction with the probe 11 (which includes an
ultrasound transducer) is used to determine the power at every relevant
velocity in a target region of the subject 10, over time, in a
conventional manner. This may be accomplished by generating pulsed
ultrasound beams, picking up the reflected energy, calculating the
Doppler shifts, and processing the data thus obtained to provide the
matrix of power and corresponding velocities of the ultrasound
reflectors. One example of a suitable Doppler ultrasound machine 12 is
the Sonara/tek pulsed Trans-Cranial-Doppler device (available from
Viasys, Madison, Wis., US), which is a pulsed Doppler system. The Doppler
ultrasound machine 12 sends the data that it captures to a personal
computer 13 that is loaded with software to generate a conventional
Doppler ultrasound display (e.g., on a monitor associated with the
computer 13) in which the x axis represents time, the y axis represents
velocity, and power is represented by color. Suitable software for
controlling the ultrasound parameters is also available from Viasys. Note
that in alternative embodiments, the functions of the Doppler ultrasound
machine 12 and personal computer 13 may be combined into a single device.

[0035] Preferably, an ECG system 14 is also provided. The ECG system 14
interfaces with conventional ECG leads 15 and generates an output in any
conventional manner. The output is preferably synchronized in time with
the Doppler ultrasound machine 12 so that both an ECG and ultrasound
display can be displayed on the same time scale. The output of the ECG
system 14 is provided to the personal computer 13 in any conventional
manner. In alternative embodiments, it may be combined by the Doppler
ultrasound machine 12 instead.

[0036] A standard TCD probe such as a 21 mm diameter, 2 MHz sensor with a
focal length of 4 cm may be used as the probe 11. Suitable probes are
available from Viasys for use with their Sonara/tek machines.
Conventional probes for making Doppler ultrasound measurements of
peripheral or cardiac blood vessels may also be used. These applications,
however, typically use narrow beams, often shaped using a phased array
transducer, to provide a high spatial resolution that is helpful for
making geometrical characterization of the relatively small targets.
While these narrow beams can produce usable results in the context of
TPD, some preferred alternative embodiments use relatively wide beams,
for example beams with an effective cross section of at least 1/2
cm2 (e.g., between 1/2 and 3 cm2). This may be accomplished by
using a smaller transducer, and by using single element transducers
instead of phased array transducers that are popular in other anatomical
applications. When a wider beam is used, the system can take advantage of
the fact that the lungs contain relatively large complexes of unspecified
geometrical shape consisting of blood vessels (both arteries and veins)
and their surrounding lung tissues.

[0037] Note that since imaging the lung with ultrasound is impossible
because of the scattering, one has to scan for targets without
guidelines, except for the known anatomy. Note also that scattering
lowers the advantage of scanning by either phase array or by mechanical
means. Furthermore, since the whole lung depth induces scattering, CW
(continuous wave) ultrasound is less effective than PW (pulsed wave)
Doppler ultrasound for pulmonary applications. Therefore, some preferred
embodiments utilize PW ultrasound with relatively wide beams. Optionally,
such embodiments may employ multiple sensors positioned on the surface of
the body.

[0038] Optionally, specially selected or designed ultrasound probes and/or
suitable beam power control may be used, including dynamic adjustable
beam shape and size so as to enable measurement from variable tissue
volumes. Note that in contrast to when Doppler is used for other tissue
targets, here the average and integral of signals originating from
relatively large volumes contain valuable information.

[0039] In addition to the standard software for generating a display from
the Doppler signals, the personal computer 13 preferably includes
software for activating the TPD and selecting the desired operating mode,
display mode, and storage modes. The personal computer 13 also includes
or has access to appropriate data storage resources (e.g., local or
remote hard drives). The personal computer 13 preferably processes the
original velocity-and-power vs. time data using one or more noise
reduction (NR) algorithms that are optimized to minimize the noise
created by the signal scattering and attenuation by the lung tissue.

[0040] One preferred approach to noise reduction involves two
phases--averaging and edge detection. In the first phase, an averaged
signal from a number of cardiac cycles is obtained by averaging the
power/velocity data of N characteristic signals, where each of the N
signals preferably represents a single cardiac cycle. N is preferably an
integer between 4 and 20 (e.g., 10). Preferably, each signal is bounded
by an R-wave at each end, although in alternative embodiments other
points on the cardiac cycle may be used as a time reference point. The
calculated averaged signal is assumed to characterize the spectrogram
behavior for the subject, and therefore is the basis on which the
relevant features are later determined. Note that while it is preferable
to perform this averaging phase, in alternative embodiments this phase
could be skipped and subsequent processing could be performed on data
from a single cardiac cycle.

[0041] The second phase is edge detection and envelope calculation. In
this phase, we delineate, in regards to both amplitude and time, the
power and velocity signal tracings vs. time, and thereby separate the
sections that represent the blood vessel movement (i.e., the signal) from
the noise. One or more noise reducing algorithms may be used during this
phase. In one preferred embodiment, two specific edge detection
algorithms, referred to herein as algorithm A and algorithm B, are
applied to the data. Both algorithm A and algorithm B are applied on the
averaged signal and calculate the edge (i.e., envelope) between the
signal and the noise in the averaged image.

[0042] Algorithm A is a local, one-dimensional method in which the edge
(eA) between signal and noise at a given time is defined according
to the statistics of the data at the proximity of this time only. This
algorithm includes two steps: In the first step, we define, at any given
time (ti), a threshold `thr(ti)` for each power spectrum A(ti) by
searching for a region of lowest energy in the proximity of ti. We then
set thr(ti) to be equal to the highest power level in this region. Next,
we apply thr(ti) on A(ti) and deem all parts of A(ti) above thr(ti) as
corresponding to movement regions and all other parts as corresponding to
noise.

[0043] In the second step of Algorithm A, we refine the initial
distinction between flow and noise by using the statistics of noise: In
this step, we assume down estimation (flow being included in noise
region); adjust envelopes detection to exclude flow pixels from noise
regions; and identify pixels of flow in noise regions by their relatively
high values. Symbolically, this can be represented by the following three
steps: [0044] (a) For each t={1,2, . . . N}, calculate P(t)={mean of A(t)
in noise region} [0045] (b) Define a threshold `thr2` which is based on
the average and std of {P(1),P(2), . . . P(N)} [0046] (c) For each t'
where P(t')>thr2, reduce P(t') by raising upper envelope or lowering
the lower envelope until P(t')<=thr2. For better results, steps
(a)-(c) are preferably repeated a number of time (e.g., 10 times).

[0047] Algorithm B is an edge detection algorithm that treats the data as
two-dimensional image. In this method, the signal is seen as an object
surrounded by noise which is segmented out of it, and the edge (eB)
is calculated accordingly. This segmentation method is an implementation
of the Chan-Vese algorithm. (See Chan T. F., Vese L. A., Active contours
without edges. Image Processing IEEE, Transactions on, Volume 10, Issue
2: 266-277 (February 2001), which is incorporated herein by reference).

[0048] The edge calculated by Algorithm A
(eA=[eA(t1),eA(t2), . . . ]) is then combined with the
edge calculated by Algorithm B (eB=[eB(t1),eB(t2), . . .
]). One suitable approach to combining those two edges is by assuming
that the desired edge passes between the two edges that were found. This
may be done using a variety of approaches. One approach is take a simple
average of the results from algorithm A and algorithm B at each point.
Another approach for combining those two edges is to create an array of
weights (w=[w(t1),w(t2), . . . ]) as follows: (1) the power levels of the
image at the gap are integrated along time; (2) the result is linearly
transformed to have a maximal value of `1` and minimal value of `0`; and
(3) the output for the edge at a time point ti is then defined by the
following equation: e(ti)=w(ti)*eA(ti)+(1-w(ti))*eB(ti).

[0049] The resulting output is preferably smoothened via a one-dimensional
median filter (e.g., of order 3) and displayed, and FIG. 2 depicts an
example of the resulting output. Note that in alternative embodiments,
only one algorithm (i.e., either algorithm A or algorithm B or a
different NR algorithm) may be used, either taken alone or combined with
other NR algorithms.

[0050] FIG. 2 depicts the velocities 22 of the ultrasound reflectors in
the right lung of a normal subject obtained using a 2 MHz Doppler
ultrasound system with the probe positioned about 3 cm to the right of
the sternum and 7 cm up from the level of the tip of the xiphoid bone
(about the 4th intercostal space). The ultrasound beam was roughly normal
to the chest surface. In FIG. 2, darker regions correspond to higher
powers. A conventional ECG 24 is preferably also displayed on the bottom
of FIG. 2. Similar recordings were obtained from recordings at depths
(gates) of up to 14 cm and from the left lung in areas not dominated by
the heart. Maximal signal strength over the right lung was recorded at a
depth of 8-9 cm below the surface.

[0051] The same pulse repetition frequency (PRF) that is used in
conventional TCD systems (i.e., 3-10 kHz) may be used for TPD systems.
However, TPD sonograms 22 includes of a number of medium velocity signals
that have the same periodicity as the cardiac cycle and usually reach
values only up to about 30 cm/sec. Due to these relatively low peak
velocities (as compared to Doppler flow measurements in large arteries),
the TPD PRF used may be set to a value that is lower than standard pulsed
Doppler systems. By lowering the PRF to between 1-3 kHz, the effective
beam penetration depth can be increased. This is important as ultrasound
velocity in the lung is about 30-50% lower than in fat, muscle etc. thus
lowering the effective penetration depth. Preferably, the software is
configured to take this lower velocity into account. The transition point
where the signals originating in the lung can be detected by recognizing
the shallowest point at which the lung signals (i.e., signals with very
large returns) appear. Note that measurements from different lung depth
result in very similar tracings, and that the traces for other apparently
normal subjects had generally similar characteristics.

[0052] It is seen that, at each polarity (positive or negative), one can
usually identify five significant features with relatively high energy
and a roughly triangular shape. These five features are schematically
illustrated and numbered #1-5 in FIG. 3. Each of these features includes
a positive component (i.e., positive velocities, indicating that the flow
direction is towards the probe) and a corresponding negative component
(i.e., negative velocities, indicating that the flow direction is away
from the probe), with a high degree of positive/negative symmetry. Thus,
each of these features indicates simultaneous movements in opposite
directions. As seen in FIG. 3, these features are synchronous with the
cardiac cycle (note the R waves 26 in the ECG 24).

[0053] Theory of Operation

[0054] The above described signals recorded over the lungs appear to have
a unique origin. As is well known the lungs consist of a very large
number of alveolar ducts, alveolar sacs and alveoli which can be regarded
as miniature gas volumes encapsulated by a very thin membrane. The
alveoli, which can be assumed to be reasonably represented by spheroids,
have dimensions in the range of 50-150μ. When exposed to ultrasound
waves these natural lung components resemble in many respects ultrasound
contrast media used in sonography. (Ultrasound contrast agents are
gas-filled microbubbles with a high degree of echogenicity, i.e., the
ability of an object to reflect the ultrasound waves.) The echogenicity
difference between the alveoli and soft tissues is very large and
therefore most of the energy is reflected.

[0055] Although scattering makes it impossible to obtain ultrasound images
of lung structures, it is actually helpful in detecting movement of the
highly reflective border between soft tissue and alveoli. Movements of
this border are induced by respiration and even more so by cardiac
contraction and mechanical pulse waves travelling in the blood and the
pulmonary blood vessels. It is well known that the pulmonary blood
vessels have a very high compliance (i.e., much larger than that of the
systemic circulation), and the air filled alveolar tissue surrounding the
vessels is highly compressible. Thus, pressure waves in the pulmonary
arteries and veins result in significant changes in their diameter. These
changes in turn move the highly reflective border, compressing and moving
the alveoli, alveolar sacs, etc. in their vicinity. As the ultrasound
propagation velocity in tissue and air are very different, there is a
mechanical coupling mismatch at their border resulting in high
echogenicity and strong ultrasound reflections, which in this case is
from a moving reflector that results in Doppler shifts. These reflections
are often on the order of 100 dB above the noise level (in comparison to
typical intensities measured from blood flowing in arteries, which are in
the range of 30-40 dB above noise level). Because these signals are so
strong, the returns are picked up by the Doppler system even though they
may be partially masked by a layer of stationary lung tissue, which
attenuates ultrasound energy by about 40 dB/cm.

[0056] FIGS. 4A and FIG. 4B illustrate the differences between
conventional Doppler signals and the signals picked up by TPD through the
chest wall. FIG. 4A illustrates the "classical Model" of clinical Doppler
measurements in which the device measures the Doppler frequency shift
resulting from blood flow 42 in arteries and veins, or more specifically
from the movement of the erythrocytes 43 (which reflect the ultrasound
waves) through those vessels 44.

[0057] FIG. 4B illustrates the origin of the Doppler signals picked up
using TPD. Here the changes in pressure induce changes in vessel diameter
because as the heartbeat generates pressure pulses that urges the blood
32 through the vessel, the vessel walls 34 momentarily bulge outwards and
compress the air filled alveoli, alveolar sacs, etc. 35 that surround
them. The Doppler shifts of the reflected ultrasound induced by the
moving vessel--alveoli border are translated to power-and-velocity vs.
time plots and displayed by the TPD system. It is expected that the
majority of these signals are generated by small and intermediate size
arteries and veins. A unique feature of signals generated in this mode
(as opposed to those generated by the flow of blood in the rest of the
body) is their bi-directionality. This phenomenon is likely because the
lung parenchyma encircles the blood vessels from all sides so that
regardless of the relative beam direction, the closer borders move
towards the beam source while those at the far side move away from it. As
a result, similar signals of opposite polarity are generated. In some
cases, as depicted in FIG. 2 the signals seem almost perfectly
symmetrical. Such symmetry is rarely seen in non-pulmonary records of
blood flow.

[0058] It is notable that with conventional Doppler measurements of blood
flow through vessels, where the movement is the blood flow itself, the
probes are positioned so the ultrasound beam is as parallel as possible
to the flow axis to obtain maximal velocity. In contrast, the motion that
gives rise to the TPD measurements described herein is perpendicular to
the direction of blood flow, so the optimal position is normal to the
flow axis and parallel to the vessel radius. But since there are so many
blood vessels in the lungs, positioning is less critical in the context
of TPD (as compared to conventional Doppler measurements of blood flow
through vessels).

[0059] Since the features in FIG. 2 always have a repetition cycle
corresponding to the R-R interval of the ECG 24, we have concluded that
they must originate from structures that reflect ultrasound energy while
moving in synchrony with the heart beat. These entities could be the
heart itself, the blood flowing in the pulmonary blood vessels, the
pulsating blood vessels, or their junctions with alveoli, alveolar sacs,
air, etc.

[0060] The recorded signals will be referred to as--Lung Doppler Velocity
Signals, (LDVS). FIG. 5A compares a typical LDVS 52 of a normal subject
with tracings 53, 54 of blood flow velocity in both a pulmonary artery
and vein, for a single cardiac cycle, with the cardiac cycle durations
normalized to the same time scale (note the R-waves 26 of the ECG 24).
Significant correlation is present. FIGS. 5B-E compare the LDVS 56 of
normal breathing (FIG. 5B) with those recorded during various respiratory
maneuvers over a number of cardiac cycles. For example, during
breath-holding at FRC (functional residual capacity) (FIG. 5C), the
features 57 have normal shape and velocity but attenuated intensity.
During a Valsalva maneuver (FIG. 5D) in which the chest cavity pressure
is greatly elevated, the features 58 are seen to virtually disappear. In
contrast, during a Muller maneuver (FIG. 5E), which generates negative
pressure within the chest cavity, both the velocity and signal power of
the LDVS 59 increase.

[0061] The synchronization of the five features (#1-5) with the heart beat
and associated mechanical events indicates that the signal source is
related to pulsations generated by the heart and blood vessels, and the
strong modulation of the features by respiratory maneuvers (see FIGS.
5C-E) indicates that the state of the lung parenchyma strongly affects
their shape. The fact that similar signals are recorded throughout the
lungs, in spite of the strong mechanical dumping properties of the lung
parenchyma, rules out direct involvement of the heart and large blood
vessels. Thus, it is most likely that the spread of the pulsations is by
propagation along the blood vessels in the lungs, including the
relatively small ones.

[0062] Based on the theory of operation set forth above, we interpret the
five features depicted in FIGS. 2 and 3 as follows: Feature #1, which is
usually very prominent, appears shortly after the R wave, and coincides
with the systolic ventricular contraction. Feature #2, which has lower
peak velocity, coincides with the T wave of the ECG and repolarization
and ventricular relaxation. Feature #3, which is often double humped and
is of relatively longer duration, seems to appear mainly during the
diastolic rapid filling phase. Feature #4, which typically has a low peak
velocity, corresponds to the diastasis, the latter part of which is often
not associated with a detectable signal. Feature #5, which is usually of
high peak velocity, coincides with atrial contraction.

[0063] The relative amplitudes, rise times and fall times, durations etc.
of these five features thus provide information regarding the blood flow
hemodynamics, passive mechanical properties of the various
cardio-vascular system components, as well as the active (contraction)
forces. In addition, the displays provide information related primarily
to the pulmonary system.

[0064] To verify the theory that the returns are generated by a moving
tissue-air boundary, a Doppler sonogram was made using a phantom where
pseudo-blood (Doppler test fluid 707, ATS Laboratories Inc. CT, USA)
incorporating miniature air bubbles (under 0.5 mm) was flowing in an
appropriate vessel. In the sonogram the bubbles appear as bright "blips".
The power spectra of the flowing pseudo blood and bubbles reveal that the
peak power generated by the moving air bubbles is about 40 dB higher than
that of flowing pseudo-blood and coronary flow recorded under similar
conditions. These results are compatible with the theory set forth above.

[0065] Measurements were taken on 10 normal volunteers aged 27-72 over the
right or left lung by means of an ultrasound sensor positioned over the
chest wall of a sitting or supine subject. A 21 mm, 2 MHz sensor having a
focal length of 4 cm was impedance matched with the chest wall by
standard ultrasound gel. Measurements were made from different positions
over the chest wall using a pulsed TCD device (Sonara/tek, Viasys,
Madison, Wis., USA) at a pulse repetition rate (PRF) of 3 kHz. The
transmitted pulse power was up to 10% of the allowed maximal ISPTA.3 (492
mW/cm2). The subjects were connected to a standard three lead ECG
(Norav Medical Ltd, Yokneam, Israel) the output of which was included in
the display.

[0066] Observing the resulting velocity-and-power vs. time traces can
provide diagnostic information on the mechanical properties of the
pulmonary parenchyma, in general and at specific locations when those
traces deviate from the expected normal traces. This may include
information related to the tissue structure (which may be relevant to
emphysema, fibrosis, atelectasis, etc.), vasculature, or the presence of
fluid in or around the alveoli (as in congestive heart failure or
pneumonia, vascular events such as emboli & hemorrhage), etc. These
deviations from normal can result from changes in the elastic properties
as well as the mass of the various tissue elements as well as their
spatial distribution. Such changes will result in global or local
corresponding changes in the power spectra profiles, time constants,
durations, or amplitudes (relative or absolute) of the traces.
Physiological manipulations such as deep inspiration, forced expiration,
breathe holding, Valsalva maneuvers, exercise, etc. may be used to
enhance the diagnostic capabilities. Note that the ultrasound waves
reflected from any intra-pulmonary element are modified as they pass
through the lung parenchyma that intervenes between them and the chest
wall. This tissue acts as a mechanical filter of specific
characteristics. These characteristics depend on the state of the
relevant parenchyma, such that the power spectra of the signals that pass
through this filter reflect on the filter characteristics for acoustic
signals as described by Gavriely N., Y. Palti & G. Elroy (Spectral
Characteristics of Normal Breath Sounds, J. Appl. Physiol. 50: 307-314
(1981), which is incorporated herein by reference).

[0067] Optionally, the signals from a single subject may be averaged over
a number of cardiac cycles using the R wave 26 of the ECG 24 as a
reference point. FIG. 6, for example, depicts an average 62 of ten
cardiac cycles from a normal subject, recorded over the right lung. Five
features #61-65 can be seen, corresponding to features #1-5 discussed
above. The traces were generally similar for other normal subjects.

[0068] Detection and Characterization of Cardiac Function

[0069] One useful application of the TPD system described herein is as a
tool for indirectly ascertaining the function of the cardiac system
through TPD measurements of the lungs. This is possible because the
outcome of the cardiac activities propagate along the pulmonary blood
vessels from their origin in the heart to the whole lung volume. A number
of clinically significant deviations from normal mechanical cardiac
activity can be detected and characterized using TPD in this way, and
some examples are given below.

[0070] FIG. 7A depicts the changes from the normal pattern of lung signals
in cases of arrhythmia due to atrial extra-systoles, which is a type of
additional abnormal cardiac contraction. The left side of FIG. 7A depicts
signals typical of a normal sinus rhythm, and the right side depicts the
appearance of an atrial extra-systole 71 (i.e., the signals generated by
an early electrical beat produced by the sinus node) that propagates to
the ventricles. These signals are basically a duplicate of the normal
rhythm complex, i.e. they include and extra atrial contraction (feature
#5) followed by an extra ventricle contraction (feature #1) and ventricle
relaxation (feature #3). When they occur early enough, the atrial
contraction signal (feature #5) may superpose in time over previous
ventricular relaxation (feature #3). FIG. 7B illustrates the
characteristics of a signal produced by an atrial extra-systole 73
resulting in an atrial contraction (feature #5) that does not propagate
from the atrium to the ventricles, as manifested by the absence of
features #1 and #3 after the abnormal additional feature #5*.

[0071] FIG. 8 illustrates signals produced by Extra-Systolic contractions
(feature #1*) generated by electric abnormal activity 82 in the
ventricle. FIG. 9 depicts signals corresponding to contractions of
ventricular origin (#1) in a patient suffering from atrial fibrillation.
This condition is apparent from FIG. 9 because feature #5 (representing
atrial contraction) is missing. It is also seen that the characteristics
of the ventricular extra-systoles are very different from those of the
atrial extra-systoles, reflecting the large differences of the nature of
the mechanical activity. Such recorded tracings can help the physician
determine the pathway of propagation of the abnormal activity.

[0072] The presence of any of the abnormal features discussed above in
connection with FIGS. 7A, 7B, 8, and 9, can therefore be used as an
indication that the patient has the corresponding problem. This may be
accomplished visually, by looking at the displays and recognizing the
relevant features. In alternative embodiments, pattern recognition
software may be used to recognize the relevant features automatically.

[0073] Multi-Position Measurements

[0074] TPD measurements may be taken from different lung depths, and such
measurements usually show very similar tracings indicating a wide spread
of the signals in the lung volume. Measurements may also be taken from
different positions on the subjects' body, such as over the intercostal
spaces (e.g. between the 2nd and 3rd ribs or between the 5th and 6th
ribs) as well as from positions over the ribs. When such measurements are
taken at multiple positions, in some cases there are significant
differences between the signal shapes, velocities, and power measurements
taken at each position. The inventors have recognized that such
recordings in general and specifically recording differences may be used
to help diagnose certain physiological conditions.

[0075] In one example, measurements were made on two chronic obstructive
pulmonary disease (COPD) patients' right lungs at three different
positions locations over each patient's right lung: an upper zone at the
level of the 2-3 ribs, a middle zone at the level of the 4th rib, and a
lower zone at the level of the 5-6 ribs. Unlike the normal subjects in
which the measurements taken at the upper, middle, and lower positions
were very similar, in the COPD patients the signals at the upper zone
were significantly smaller than those in the middle zone, which were in
turn significantly smaller than the signals at the lower zone. In
addition, the signal shapes (e.g., the degree of symmetry in velocity and
power) were also different in the different zones. This deviation from
the normal situation can be used as predictor for the presence of COPD.
Similarly, other deviations from the normal situation can be used as
predictor for the presence of other abnormal conditions.

[0076] The average peak positive and negative velocities for features #1,
3, and 5 were measured for a group of patients (including normal
patients, COPD patients, sarcoidosis patients, and a fibrosis patient)
from each of those three positions (i.e., upper, middle, and lower). That
experimental data is depicted in FIGS. 10A-C, with positive and negative
velocities on the y-axis. The normal patients are the ones on the left,
the patients between FS and DUL had COPD, the patients between BAD and
BUJ had sarcoidosis, and the patients between RL and EHOE had fibrosis.
In FIG. 10A, each group of 3 Bars (left, center, and right) represents
the results of the average peak positive and negative velocity (in
cm/sec) that was obtained for feature #1 in the upper, middle, and lower
zones, respectively, for each patient. FIGS. 10B and 10C depict
corresponding data for features #3 and 5. Note that the labels U, M, and
L (which denote the upper, middle, and lower zones, respectively) have
only been included for one patient in each of FIGS. 10A-C to avoid
clutter.

[0077] Examination of the data depicted in FIGS. 10A-C reveals that in
normal patients, the velocities for feature #1 were roughly similar in
all three zones. But in the COPD patients, the velocity was much lower in
the upper zone than in the middle zone, and the velocity was much lower
in the middle zone than in the lower zone. The same situation was true
for feature #5. The presence of those relative velocities for features #1
and 5 can therefore be used as a predictor for the presence of COPD. The
test for distinguishing between normal and COPD patients may be fixed
(e.g., COPD may be indicated if the peak velocity of the middle reading
is at least twice as large as the peak velocity of the upper reading and
the peak velocity of the lower reading is at least three times as large
as the peak velocity of the upper reading). Alternatively, the threshold
levels may be obtained using parameterization as described below. Thus,
we see that the differences between the velocities for the features at
different locations can be used to help distinguish between normal
subjects and patients with various diseases.

[0078] FIG. 11A is a graphical representation of the differences between
normal and COPD subjects, based on the averages of those two groups of
patients, which highlights the distinction between the peak velocities
for features #1 and #5 at the upper, middle, and lower zones.

[0079] Optionally, the above described data may be combined with "power
sonogram" data, as described in U.S. application Ser. No. 12/771,091,
filed Apr. 30, 2010, which is incorporated herein by reference. The
personal computer 13 (show in FIG. 1) should then be programmed to
extract the power data from the ultrasound returns as described in the
'091 application. FIG. 11B demonstrates that power data so obtained can
serve to differentiate between normal subjects, patients with COPD, and
patients suffering from pulmonary fibrosis. In the latter, connective
tissue that conducts ultrasound energy well replaces the air filled
alveoli and thus one obtains higher total power values. Note also that in
the case of fibrosis (in contrast to the normal and COPD cases) the
largest power signal is often recorded from the upper lung segment. This
may be used as a predictor for the presence of fibrosis.

[0080] Distinctions between the Congestive Heart Failure (CHF), pulmonary
emphysema, and edemas can also be characterized by differences their
Doppler signatures. For example, in edema patients the power will be
lower than normal, but in CHF patients the power may be higher than
normal due to the excess fluid in the lungs (which provides less signal
attenuation that the air that would ordinarily be there in a normal
patient). The power distribution between the different lung zones may be
altered with the local changes in the lung parenchyma and vasculature.
These distinctions may be detected using TPD and relied on to diagnose
those conditions, either visually from the displayed power-and-velocity
vs. time displays, or automatically using appropriate pattern recognition
or parameterization software. Similar concepts may be used for other
pathologies.

[0081] Measurement of Pulmonary Blood Pressure

[0082] Pulmonary blood pressure may be elevated as a consequence of
numerous conditions as well as pulmonary and cardiac diseases such as
CHF. Although detection, characterization, and follow up of pulmonary
hypertension (PH) is important, all of the prior art technologies are
problematic. In some cases, indirect and inaccurate estimation can be
made using complex ultrasound imaging. But the only reliable measurement
method is invasive--introducing a measuring catheter through the heart
into the pulmonary blood vessels. In contrast, TPD can be used to measure
the pulmonary blood pressure rapidly, simply, effectively, and
non-invasively.

[0083] In a classical sphygmomanometer, the pressure around a peripheral
artery (e.g., brachial, radial) is elevated while the arterial pulse is
being monitored and the maximal and minimal pressure is determined on the
basis of the changes in the vessel pulsations. Within this framework the
systolic blood pressure is determined by the pressure at which blood flow
and pulsations cease. As explained above, the signals recorded by the TPD
reflect pulsations in the pulmonary blood vessels. These vessels are
surrounded by lung parenchyma that consists of multiple air compartments
the pressure of which can be controlled. Because of this, it becomes
possible to determine the pulmonary blood pressure by elevating the
pulmonary air pressure and monitoring the TPD signals to determine the
blood flow and vessel pulsations through the blood vessels in the lungs
under various pressure conditions.

[0084] FIG. 12 is a block diagram of a system for performing such a
measurement. During the entire procedure, the TPD Probe/sensor 225 should
be positioned on the patient's chest 226 and the lung signals are
processed by TPD 224 recorded and displayed. To obtain readings, the
pulmonary air pressure is elevated and then returned to normal. One way
to vary the pulmonary air pressure is to have the patient 201 inflate his
lungs to a predetermined degree and then blow forcefully into a tube 200
connected to the air reservoir 212 (e.g., via a disposable mouth-piece
210). In this case, it is mainly the patient's diaphragm that increases
the pressure. The pressure is preferably displayed on display 218 (or
pressure gauge, not shown) for the patient to see, and the patient is
instructed to keep the pressure at a requested level using a blowing
action. The patient is also instructed to keep his glottis open so that
the pressure equalizes in the whole system. If this approach is used, the
pump 215 and associated hardware and software can be omitted. Another way
to vary the pulmonary air pressure is to elevate the pressure in the
lungs 222 using a pump 215 under control of controller 216 and processor
217 so as to drive the lung pressure to the desired level. When a pump is
used, feedback is preferably obtained using a pressure sensor 214. Note
that in either situation, the desired pressure level may be varied over
time to follow a desired curve (e.g., by first increasing the pressure
and then letting it drop slowly, either gradually or in steps).

[0085] FIG. 13 depicts how the TPD signals change in response to a gradual
elevation of the lung air pressure, and the resulting changes in the
properties of the blood vessels. When the pressure is increased, the
blood vessels will eventually collapse (either completely or partially)
at the point when the external pressure equals or exceeds the blood
pressure, which occurs between 11 and 11.5 seconds in FIG. 13 (denoted by
the arrow 132). This phenomenon is similar to the way the blood flow
stops when the pressure imposed by a conventional blood pressure cuff
pressing on the brachial or radial arteries exceeds a particular level.

[0086] FIG. 14 depicts an example of changes that occur in a patient when
the lung air pressure is elevated and maintained at the elevated level.
The changes in the amplitude and characteristics of the different
features (#1-5, discussed above) at the pressure levels indicated at the
right carry information regarding the various levels of the blood
pressure in the relevant vessels. Note that the variations of each of the
five features #1-5 may occur at different pressures. For example, the
positive part of signal #1 disappears at a pressure of about 16 mm Hg,
while the negative signal, 1*, remains practically intact. The negative
part of signal #3 (3*) is already attenuated at a pressure of about 10 mm
Hg, while the positive part is only attenuated at higher pressures.
Signal #4 is also practically eliminated at a pressure of 10 mmHg.

[0087] Note that normal pulmonary blood pressures (as measured by
invasively introducing pressure sensors into the relevant blood vessels)
is usually quoted as 10-15 mm Hg for the diastolic and 25-30 and for the
systolic pulmonary artery pressure, and about 8-10 mm Hg for the
pressures at the venous side (pulmonary vein) of the pulmonary
circulation. But since these values are for the main large vessels into
which the pressure transducers are introduced, the lower pressure levels
in the TPD-based measurements make sense because the pressures in the
smaller vessels are most likely lower (although they have yet not been
documented). One can therefore relate the pressures measured using TPD to
the appropriate elements of the pulmonary circulation.

[0088] The lung air pressure may be elevated gradually in order to record,
in a single pressurization, the variation of the features #1-5 under a
range of pressures. An example of such a measurement is given in FIG. 15,
in which the pressure was slowly increased, then maintained at a high
plateau of about 2 kPa, as depicted in the middle panel 154. The recovery
of the blood flow through the small pulmonary vessels in response to a
decrease in pressure can be seen on the right section of the top panel
152. Note that the pressure elevation in this example involved a lung
inflation to a total lung capacity of 3 L, as measured by spirometry, as
depicted in the lower panel 156.

[0089] The interpretation of the above described signal changes and the
determination of the lung circulation pressures can be made by the
physician based on when the various TPD features #1-5 shrink or
disappear. Alternatively, suitable pattern recognition software may be
used to automatically detect the relevant changes.

[0090]FIG. 16 depicts the power level of the signals the TPD records when
the pulmonary pressure is elevated. The pulmonary vascular bed pressure
may be determined from the point 162 where the power amplitude approaches
zero (or falls to less than 10% of the maximum). FIG. 17A depicts the
situation when the pressure is elevated to different levels and
maintained there for relatively long periods of time (e.g., 10-20 sec).
The signals attenuate as described and approach zero at the pressure
level corresponding to that of the venous circulation (12 mm Hg in the
example depicted). At a new pressure elevation, for example to 15 mm Hg
in the FIG. 17A, the blood flow and pulsations stop. However, as blood
flow stops, the pressure drop along the circuit nulls so that the whole
system gradually attains the high systolic pressure and all the vessels
are reinflated and therefore with time (determined by the capacity of the
vasculature) the blood flow and the pulsations reappear. This is seen in
the corresponding measured power points in FIG. 17A. Such pulsations will
be recorded until a pressure elevation to a value equal to or exceeding
the systolic arterial pressure is applied and maintained. The pressure
where there are no pulsations whatsoever corresponds to the pulmonary
arterial systolic pressure. Thus, there are two points where the curve
approaches the zero power level (or falls to less than 10% of the
maximum). The first point 172 where the curve approaches the zero power
level (i.e., with a pressure reading of about 12 mm Hg for this subject)
is believed to correspond to the pulmonary pressure at the venous side.
The second point 174 where the curve approaches the zero power level
(i.e., with a pressure reading of about 20 mm Hg) is believed to
correspond to the pulmonary pressure at the arterial side.

[0091] FIGS. 17B and 17C compare the total power readings for a normal
subject (FIG. 17B) and a subject with pulmonary hypertension (FIG. 17c),
respectively. The higher pressure readings are evident in the
hypertension subject. The total power is obtained by summing the power at
every relevant velocity from the power and velocity data (i.e., including
all the features #1-#5, discussed above) in a known time interval.

[0092] Thus, it becomes possible to estimate the pulmonary blood pressure
of a patient, by sequentially obtaining, using transthoracic pulmonary
Doppler ultrasound, power and velocity data from at least one of the
patient's lungs at each of a plurality of different air pressure levels.
The patient's pulmonary blood pressure can then be estimated based on the
obtained data.

[0093] The level of pulmonary blood pressure of the patient can be
determined by monitoring the total power as the air pressure changes.
This level may be determined by providing a numeric estimate of what the
blood pressure is, as described above. In alternative embodiments, a
binary indication of pulmonary blood pressure level may be provided,
where one binary state indicates normal pulmonary blood pressure, and the
other binary state indicates PHT (pulmonary hypertension), as described
below.

[0094] One way to generate a binary indication of whether or not a patient
has PHT is to elevate the air pressure in at least one of the patient's
lungs to a level where blood flow would be expected to drop in healthy
patients (i.e., patients who do not have pulmonary hypertension). Once
this is done, power and velocity data from at least one of the patient's
lungs is obtained while the air pressure is elevated. If the total power
(computed from the obtained power and velocity data) is above a threshold
(e.g., 20% of the total power that one would expect to see if the air
pressures in the patient's lungs was not elevated), then we have an
indication that the patient has PHT. Examples of the degree of pressure
elevation needed to do this test could be 10, 15, or 20 mm Hg. The test
would be more reliable at higher pressures.

[0095] Another way to generate a binary indication of whether or not a
patient has PHT is to use a classification algorithm. This approach
relies on the extraction of classification features from the power and
velocity data obtained by TPD. Examples of such classification features
include: the velocities (peak, average, median etc.) and the power
integral values corresponding to the velocities of the different features
(for example, features #1, #3 and #5) in a number of locations over the
chest wall (for example, Inter-Costal-Spaces ("ICS") #2, #4 and #6) and
selected distances from the surface.

[0096] One example of a preferred classification algorithm used the
following 4 classification features: [0097] A=The ratio of peak
velocities in feature #3, between ICS 4 and ICS 6. [0098] B=The ratio of
power integral values in feature #3, between ICS 4 and ICS 2. [0099]
C=The ratio of peak velocities in feature #1, between ICS 2 and ICS 6.
[0100] D=The ratio of power integral values in feature #5, between ICS 4
and ICS 6.

[0101] These four features were normalized to [0-1] range, and then
applied to Fisher's linear discriminant which linearly combines the
selected features into one discriminative feature. Classification based
on 33 normal subjects and 20 PHT subjects yielded the following formula
for designating a patient as either normal or PHT: X=4.8499 A+6.3762
B-3.3423 C-4.6710 D. In this example, the optimal decision threshold is
0, and a patient is designated as either PHT if X>0 or as normal if
X<0. Fisher's linear discriminant is described in Ronald Fisher (1936)
The Use of Multiple Measurements in Taxonomic Problems In: Annals of
Eugenics, 7, p. 179-188, which is incorporated herein by reference.

[0102] Another example of a preferred classification algorithm used the
same 4 classification features A-D defined above, and a conventional
Support-Vector-Machine (SVM) with Radial Basis Function (RBF) kernel. SVM
is described in Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: A
library for support vector machines. SVM is also described in Press, W.
H. et al. (2007) "Section 16.5. Support Vector Machines" Numerical
Recipes: The Art of Scientific Computing (3rd ed.) New York: Cambridge
University Press. Both of these references are incorporated herein by
reference.

[0103] In the 5-fold Cross Validation the subjects are randomly
partitioned into 5 subsets. Of the 5 subsets, a single subset is retained
as the validation data for testing the model, and the remaining 4 subsets
are used as training data. The cross-validation process is then repeated
5 times, with each of the 5 subsamples used exactly once as the
validation data. The final result is the average between the 5
repetitions. Using classification features A-D identified above, the
5-fold cross-validation result is 90.5% (48/53) true classification.

[0104] In another preferred embodiment, instead of classification features
A-D identified above, the following four classification features E-H may
be used: [0105] E=The ratio between the peak velocity of feature #3 in
ICS 4 and in ICS 6 [0106] F=The ratio between the peak velocity of
feature #1 in ICS 2 and in ICS 6 [0107] G=The ratio between the peak
velocity of feature #5 in ICS 4 and in ICS 6 [0108] H=The ratio between
the power of feature #3 in ICS 4 and in ICS 2

[0109] Examples of classification features that may be used in other
alternative embodiments include: the duration of features #1, #3, #5; the
peak velocity in features #1, #3, #5; the peak velocity time in features
#1, #3, #5; the power integral in features #1, #3, #5; the peak of the
power integral in features #1, #3, #5; the peak power time in features
#1,#3,#5; the power in the peak velocity time in features #1, #3, #5; the
delay between the peak power times (positive-negative) in features #1,
#3, #5; the ratio between the positive and negative peak power values in
features #1, #3 and #5; the correlation between the velocity and power
values in positive/negative features #1, #3, #5; the correlation between
the positive and negative peak velocity values in features #1, #3, #5;
the power weighted peak velocity in features #1, #3, #5; the rising slope
of feature #1 and feature #5; and the falling slope of feature #1. Linear
or non-linear combinations of all the above in different ICSs and
different distances from the surface may also be used.

[0110] Automatic Feature Recognition

[0111] The discussion above makes frequent references to features #1-5.
Optionally, software that recognized the delineation between each of
those features may be implemented in the personal computer 13 (shown in
FIG. 1). Automatic feature recognition ("AFR") may be implemented on the
averaged signals discussed above in connection with FIG. 6, on a single
signal (e.g., as depicted in FIG. 2), or after the averaging operation
contained within the NR (i.e., the first phase of the noise reduction
routine discussed above). FIG. 18 is an example of automatic feature
recognition based on the latter. In FIG. 18, each of the features #1-5 is
bounded by two local minimum points on the calculated envelope, and
defined according to the relative location of its peak velocity (i.e.,
maximum point) and the averaged signals' ECG waveforms. These local
minima define the transitions 181-185 between the various features and
are denoted by dashed lines in FIG. 18. In a regular cardiac rhythm, the
features are defined in relation to the ECG signal 24 as follows: #1--the
segment with the first velocity peak after the first R-wave 26; #2--the
segment with the first velocity peak after feature #1 but preceding the
ECG's T-wave; #3--the segment with the first velocity peak after the
T-wave ends; #4--the segment bounded between feature #3 and feature #5;
and #5--the segment with the velocity peak that immediately precedes the
next R wave and next feature #1.

[0112] AFR can be useful because the absolute and relative calculated
parameters that characterize these segments may be used to classify and
diagnose a pathology and its location. These parameters are useful for
automated recognition of various conditions that rely on
parameterization, discussed below.

[0113] Parameterization

[0114] Parameterization may be used to characterize the various features
so as to diagnose and estimate the extent of various pathologies such as
COPD, Sarcoidosis, Fibrosis asthma, emphysema, pulmonary hypertension,
pulmonary embolism, tumors, arteriosclerosis of pulmonary vessels,
atelectasis, cardiac contractile dysfunction, and arrhythmia etc.
Quantification of the various parameters may be done on specific segments
and the relations between them, as well as on the variability of the
signals in the original spectrogram (i.e., before it was averaged). The
parameterization may be implemented using the approaches described in
U.S. application Ser. No. 12/700,828 ("the '828 application"), filed Feb.
5, 2010, which is incorporated herein by reference.

[0115] Some of the data is derived from the power spectra themselves as
provided by the Doppler measurements. The features of these power spectra
may also be parameterized, for example the power at specific velocities,
the average slopes of the curves, the number of different slopes at the
positive and negative features etc. Parameters may also be derived from
the velocity and power versus time tracings. The tables below contain
examples of parameters that may be used to parameterize the TPD results,
and their definitions:

[0116] Using these parameters, the learning and classifying steps may be
implemented as described in the '828 application.

[0117] Conclusion

[0118] The Doppler signatures of the following of tissues and structures
may change with pathology: pulmonary emphysema, pulmonary emboli,
pulmonary hypertension, pulmonary blood vessel stenosis & malformations,
conditions associated with pulmonary fibrosis, pneumonia, atelectasis,
pneumothorax, congestive heart failure, pulmonary solid tumors, various
cardiac malfunctions that are manifested in the pulmonary blood vessels,
tumors, and foreign bodies, etc. Thus, the lung Doppler signals picked up
using TPD may be used to provide insights and potentially valuable
diagnostic information regarding the structure and integrity of the lung
parenchyma and vasculature. TPD may therefore serve as a new non-invasive
and non-destructive tool for diagnosis of pulmonary disease & function.
It may also enable continuous monitoring of the status of a failing
pulmonary or cardio-vascular system, and help determine the efficacy and
so enable dose calibration, for optimal treatment.

[0119] An additional unique diagnostic capability of the TPD is to
determine the compliance (elastance) of the pulmonary vascular tree
components that changes in cases of arteriosclerosis and other vascular
conditions. Vascular compliance can be measured on the basis of the pulse
propagation velocity in the vessel because the more rigid the vessel is,
the faster the propagation will be. In the case of the lungs, the
propagation velocity can be determined from the delay between the time of
appearance of any of the lung signals (or their peak, etc.), at different
locations along the propagation pathway. Such delay measurements can be
made, manually or automatically by appropriate software, in the different
records obtained at different lung locations or at different depths
beneath a single location.

[0120] While the present invention has been disclosed with reference to
certain embodiments, numerous modifications, alterations, and changes to
the described embodiments are possible without departing from the sphere
and scope of the present invention, as defined in the appended claims.
Accordingly, it is intended that the present invention not be limited to
the described embodiments, but that it has the full scope defined by the
language of the following claims, and equivalents thereof.

Patent applications by Yoram Palti, Haifa IL

Patent applications in class Used as an indicator of another parameter (e.g., temperature, pressure, viscosity)

Patent applications in all subclasses Used as an indicator of another parameter (e.g., temperature, pressure, viscosity)